Method and device for reducing image color noise

ABSTRACT

A device and method for reducing color noise of an image by using a distance weight depending on a distance from a central pixel for each pixel of an image and an edge weight depending on the difference in luminance and chrominance with the central pixel is provided, which can effectively reduce the color noise of the image by using the correlation between the luminance and the chrominance and edge characteristics.

PRIORITY

This application claims priority under 35 U.S.C. §119(a) to anapplication entitled “Method And Device For Reducing Image Color Noise”filed in the Korean Intellectual Property Office on Mar. 10, 2010 andassigned Serial No. 10-2010-0021218, the entire disclosure of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to an image processing methodand an image processing device, and more specifically, to a method anddevice for reducing image color noise captured by an image sensor.

2. Description of the Related Art

In general, a digital camera or a camcorder uses an image sensor, suchas a Charge Coupled Device (CCD), a Complementary Metal OxideSemiconductor (CMOS), or the like, instead of using film. The CCD isclassified into a multiple CCD and a single CCD depending to the numberof colors focused on a pixel. A multiple CCD can provide more accurateluminance and more accurate matching with a primary color for eachpixel, as compared to a single CCD. However, the multiple CCD uses atleast three times as many sensors as those used in the single CCD inorder to detect each color component according to used color formats,such that the hardware architecture becomes complex and increaseshardware size. For this reason, the single CCD has been predominantlyused rather than the multiple CCD.

In the case of the single CCD, each pixel stores only one colorinformation of the RGB color channels. Therefore, in order to obtain thecomplete information on images, the color information of other colorchannels that is not stored in the pixel should be interpolated frompixel information adjacent to the pixel. However, when unwantedinformation is interpolated during the interpolation process, noise orartifacts, which are largely unpleasant to the eye, are generated inimages.

Therefore, in order to reduce the noise, research has been conducted inthe field of image processing. A noise reducing algorithm may beclassified into a method using a restoration mechanism, a method using afiltering mechanism, and the like. Since the restoration mechanismdepends on accurate modeling for noise, excellent results are obtainedbut the burden on hardware is increased. Therefore, a method of usinglocal probabilistic characteristics, for example, Local Linear MinimumMean Square Error (LLMMSE), or the like, has been primarily used. Arelatively simple bilateral filter approximating the LLMMSE, and thelike, has been primarily used. The following Equation (1) is a generaltype of the LLMMSE:

${{{out}\lbrack r\rbrack}\lbrack c\rbrack} = {{{{mean}\lbrack r\rbrack}\lbrack c\rbrack} + {\frac{{var}\left( {{{in}\lbrack r\rbrack}\lbrack c\rbrack} \right)}{{var\_ noise} + {{var}\left( {{{in}\lbrack r\rbrack}\lbrack c\rbrack} \right)}}\left( {{{{in}\lbrack r\rbrack}\lbrack c\rbrack} - {{{mean}\lbrack r\rbrack}\lbrack c\rbrack}} \right)}}$

where mean [r][c] represents the mean of (r,c) points, var (in[r][c])represents variance of r, c points, and var_noise represents thevariance of noise. In Equation (1), when the variance (var_noise) ofnoise is relatively larger than variance (var(in[r][c]) of signal, var(in[r][c])/(var_noise+var(in[r][c]) approximates “0” and the outputout[r][c] approximates the mean accordingly, such that noise is reduced.On the other hand, when the variance of a signal is larger than thevariance of a noise as in an edge region,var(in[r][c])/(var_noise+var(in[r][c])) approximates “1” and an output(out[r][c]) approximates an original signal in [r][c] accordingly, suchthat noise is less reduced.

An example of other color noise reducing filters may include a MeanFilter (MF), a Vector Median Filter (VMF), a Vector Directional Filter(VDF), and the like.

FIG. 1 is a diagram illustrating an example of MF, VMF, and VDFaccording to the related art. The MF depends on a method of obtaining amean of pixel values in a local region. In FIG. 1, the MF result forthree pixels having different directions and phases is

${MF} = {\left( {\frac{R_{1} + R_{2} + R_{3}}{3},\frac{G_{1} + G_{2} + G_{3}}{3},\frac{B_{1} + B_{2} + B_{3}}{3}} \right).}$

However, since the MF is a Low Pass Filter (LPF), in addition to noise,high frequency components necessary for images, such as an edge, is alsoreduced, such that the detail of images is reduced.

The median filter is a filter, which is efficient in reducing laplaciannoise, which can efficiently reduce pixels in which colors are visuallysplashed. The VMF, which is a median filter, outputs a vector having anintermediate magnitude among color vectors in a local region as aresult. For example, referring to FIG. 1, the VMF outputs a color valuecorresponding to {right arrow over (v)}₃ having an intermediatemagnitude among color vectors {right arrow over (v)}₁, {right arrow over(v)}₂, {right arrow over (v)}₃ representing three pixels as a result.That is, the VMF outputs VMF=(R₃, G₃, B₃) as a result.

The VDF is a filter that outputs a color vector having an intermediatephase among the color vectors in a local region as a result. Forexample, referring to FIG. 1, the VMF outputs a color valuecorresponding to {right arrow over (V)}₂ having an intermediate phaseamong color vectors {right arrow over (v)}₁, {right arrow over (v)}₂,{right arrow over (v)}₃ representing three pixels as a result. That is,the VMF outputs VDF=(R₂, G₂, B₂) as a result.

As described above, the methods for reducing color noise, such as MF,VMF, VDF, or the like, according to the related art, uniformly reducethe color noise to the same degree without accurately considering thecorrelation between luminance Y of images and chrominance Cb and Cr ofimages and edge characteristics.

SUMMARY OF THE INVENTION

Accordingly, the present invention has been made to solve theabove-mentioned problems occurring in the prior art, and the presentinvention provides a method and apparatus for reducing color noisecapable of keeping a high frequency component necessary for an imagesuch as an edge to vividly output the image by reducing color noisegenerated by an image sensor by using the correlation between luminanceand chrominance and edge characteristics in order to provide a highdefinition image.

In accordance with an aspect of the present invention, there is provideda method for reducing color noise, including calculating a distanceweight according to a distance from a central pixel to each pixel of aninput image in a mask having a predetermined size; calculating an edgeweight depending on a luminance and a chrominance between the centralpixel and each pixel; calculating a weight mean of each pixel by usingthe calculated distance weight and edge weight; and correcting theluminance and the chrominance of each pixel by using the calculatedweight mean.

In accordance with another aspect of the present invention, there isprovided a device for reducing color noise, including a distance weightcalculator calculating a distance weight according to a distance from acentral pixel to each pixel of an input image in a mask having apredetermined size; an edge weight calculator calculating an edge weightdepending on a luminance and a chrominance between the central pixel andeach pixel; a weight mean calculator calculating a weight mean of eachpixel by using the calculated distance weight and the edge weight; and acolor noise reducing unit correcting the luminance and the chrominanceof each pixel by using the calculated weight mean.

In accordance with another aspect of the present invention, there isprovided an image photographing apparatus, including an imagephotographing unit photographing images and converting them into imagesignals; an image signal processor processing the images into imagesignals including luminance and chrominance; a device for reducing colornoise correcting luminance and chrominance of each pixel by using adistance weight depending on a distance from a central pixel to eachpixel of an input image and an edge weight depending on a difference inluminance and chrominance between the central pixel and each pixel ofthe image; a display unit outputting an image signal from which colornoise is reduced; and a storage unit storing the image signal from whichcolor noise is reduced.

As set forth above, since the present invention reduces the color noisegenerated by the image sensor using the correlation between luminanceand chrominance and the edge characteristics, the high frequencycomponents necessary for images, such as edges, are maintained tovividly represent the detailed portions, as compared to the method ofuniformly reducing color noise by the average value in the relationshipbetween the adjacent pixels.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of the presentinvention will be more apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings, in which:

FIG. 1 is a diagram showing color vectors of three pixels havingdifferent directions and phases;

FIG. 2 is a flow chart showing each process in a method for reducingcolor noise according to an embodiment of the present invention;

FIG. 3 is a diagram showing a distance weight according to the presentinvention;

FIG. 4 is a diagram showing a device for reducing color noise accordingto an embodiment of the present invention; and

FIG. 5 is a diagram showing an image photographing apparatus accordingto an embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE PRESENT INVENTION

Embodiments of the present invention will be described with reference tothe accompanying drawings. In the following description, a detaileddescription of known functions and configurations incorporated hereinwill be omitted when it may make the subject matter of the presentinvention unclear. Further, various definitions found in the followingdescription are provided only to help with a general understanding ofthe present invention, and it is apparent to those skilled in the artthat the present invention can be implemented without such definitions.

FIG. 2 is a flow chart showing each process in a method for reducingcolor noise according to an embodiment of the present invention.

Referring to FIG. 2, the method for reducing color noise according to anembodiment of the present invention includes calculating a distanceweight in step S21, calculating an edge weight in step S22, calculatinga weight mean in step S23, and correcting luminance and chrominance instep S24. The method of reducing color noise uses the correlationbetween the luminance and the chrominance and edge characteristics,thereby making it possible to effectively reduce the color noisegenerated by an image sensor.

Specifically, in calculating the distance weight in step S21, thedistance weight is calculated on the basis of coordinates of a centralpixel within a mask having a predetermined size in an image. Thepredetermined size can be a pixel array of 5×5, 7×7, or the like.Provided that the coordinates of the central pixel within the maskhaving the predetermined size are (r_(c),c_(c)) and the coordinates ofthe pixel to be calculated are (r,c), the distance weight is determinedto be inversely proportional to the distance between the coordinates ofeach pixel to be calculated from the coordinates of the central pixel.Therefore, as the distance between the coordinates of the central pixeland the coordinates of each pixel to be calculated is increased, thedistance weight is decreased.

An example of the distance weight W_(d)(r,c) depending on the distancebetween the coordinates of the central pixel and the coordinates of eachpixel will be described in the following Equation (2):

$\begin{matrix}{{W_{d}\left( {r,c} \right)} = \frac{1}{\sqrt{\left( {r - r_{c}} \right)^{2} + \left( {c - c_{c}} \right)^{2}} + 1}} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

As represented by Equation (2), the distance weight W_(d)(r,c) may bedetermined to be in inverse proportion to the distance between thecoordinates of the central pixel and the coordinates of the pixel to becalculated, that is, between the coordinates of each pixel.Additionally, the relationship between them may be represented invarious forms.

The distance weight is introduced into the method to reduce color noiseis to apply differentially the color noise reducing ratio depending onthe distance from the central pixel, with respect to any two pixelswhere the luminance Y and the chrominance Cb and Cr are changed at thesame ratio based on the central pixel.

In mask 3-1 (5×5 size) in an image 3 shown in FIG. 3, an example of twopixels 31 and 32 having different distances from central pixel 30 willbe described. Provided that the distance weight of a first pixel 31positioned at a place where the distance from the central pixel 30 is d1is W_(d1) and the distance weight of a second pixel 32 positioned at aplace where the distance from the central pixel 30 is d₂ is W_(d2),W_(d1) has a value smaller than W_(d2). Therefore, when YCbCr of thefirst pixel 31 and the second pixel 32 is changed at the same ratio incomparison with that of the central pixel 30, the color noise reducingratio is different due to a different distance weight.

In calculating the edge weight in step S22, the edge weight is thedifference in size between the luminance and the chrominance of thecentral pixel and the luminance and the chrominance of each pixel. Theedge is a portion where the luminance or the chrominance is suddenlychanged between adjacent pixels in the mask 3-1, that is, a portioncorresponding to a contour of an object (for example, a person, animal,building, or the like) formed in the frame 3 corresponding to a subject.

In order to calculate the edge weight, an absolute value of thedifference in size between the YCbCr components forming the centralpixel and each pixel is first calculated. It is assumed that theluminance and the chrominance of the central pixel areY_(in)(r_(c),c_(c)) and Cb_(in)(r₂,c_(c)) and Cr_(in)(r_(c),c_(c)),respectively, and the luminance and the chrominance of each pixel areY_(bi)(r,c) and Cb_(in)(r,c) and Cr_(in)(r,c), respectively. In thiscase, the absolute values D_(Y), D_(Cb), and D_(C), of the difference inthe luminance and the chrominance between the central pixel and eachpixel are represented by the following Equation (3).

D _(Y) =|Y _(in)(r _(c) ,c _(c))−Y _(in)(r,c)|

D _(Cb) =|Cb _(in)(r _(c) ,c _(c))−Cb _(in)(r,c)|

D _(Cr) =|Cr _(in)(r _(c) ,c _(c))−Cr _(in)(r,c)|  Equation (3)

Next, the edge weight W_(e) _(—) _(y)(r,c) of the luminance Y, the edgeweight W_(e) _(—) _(Cb)(r,c) of the chrominance Cb, and the edge weightW_(e) _(—) _(Cr)(r,c) of the chrominance Cr are calculated by using theabsolute values D_(Y), D_(Cb), and D_(Cr) depending on the difference insize of the calculated YCbCr signal. According to an embodiment of thepresent invention, the edge weights W_(e) _(—) _(Y)(r,c), W_(e) _(—)_(Cb)(r,c), and W_(e) _(—) _(Cr)(r,c) are calculated by the followingEquation (4) depending on preset threshold values th_(e) _(—) _(Y),th_(e) _(—) _(Cb), and th_(e) _(—) _(Cr).

$\begin{matrix}{{W_{e\_ Y}\left( {r,c} \right)} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Y}}{{th}_{e\_ Y}} + 1} - 1} & {{{if}\mspace{14mu} D_{Y}} \geq {th}_{e\_ Y}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cb}}\left( {r,c} \right)}} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Cb}}{{th}_{e\_ {Cb}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cb}} \geq {th}_{e\_ {Cb}}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cr}}\left( {r,c} \right)}} = \left\{ \begin{matrix}{\frac{2}{\frac{D_{Cr}}{{th}_{e\_ {Cr}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cr}} \geq {th}_{e\_ {Cr}}} \\0 & {{otherwise}.}\end{matrix} \right.} \right.} \right.} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

In Equation (4), if the absolute value D_(Y) is the threshold valueth_(e) _(—) _(Y) or less, 0≦D_(Y)/th_(e) _(—) _(Y)≦1. The range of edgeweight W_(e) _(—) _(Y)(r,c) value becomes 0≦W_(e) _(—) _(Y)(r,c)≦1. Thatis, when D_(Y)=th_(e) _(—) _(Y), W_(e) _(—) _(Y)(r,e)=0 and whenD_(Y)=0, W_(e) _(—) _(Y)(r,c)=1.

Similarly, if the absolute value D_(Cb) is the threshold value th_(e)_(—) _(Cb) or less, 0≦D_(Cb)/th_(e) _(—) _(Cb)≦1 and the range of edgeweight W_(e) _(—) _(Cb)(r,c) value becomes 0≦W_(e) _(—) _(Cb)(r,c)≦1.That is, when D=th_(e) _(—) _(Cb), W_(e) _(—) _(Cb)(r,c)=0 and whenD_(Cb)=0, W_(e) _(—) _(Cb)(r,c)=1.

Additionally, if the absolute value D_(Cr) is the threshold value th_(e)_(—) _(Cr) or less, 0≦D_(Cr)/th_(e) _(—) _(Cr)≦1 and the range of edgeweight W_(e) _(—) _(Cr)(r,c) value becomes 0≦W_(e) _(—) _(Cr)(r,c)≦1.That is, when D_(Cr)=th_(e) _(—) _(Cr), W_(e) _(—) _(Cr)(r,c)=0 and whenD_(Cr)=0, W_(e) _(—) _(Cr)(r,c)=1.

In calculating the weight mean in step S23, the weight mean of eachpixel is calculated by using the calculated distance weight and the edgeweight.

The weight mean according to the embodiment of the present invention iscalculated using Equation (5):

$\begin{matrix}{{Y_{{wm}\_ y} = \frac{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}{\sum\limits_{y = y_{N}}^{Y_{\; N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}}{Y_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}}{{Cb}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cb}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}}{{Cr}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cr}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

As shown in Equation (5), the weight mean is calculated by the abovefour types and the weights of each edge are changed according to thetype used. In Equation (5), N in R_(N) and C_(N) represents a serialnumber of each pixel in addition to the central pixel within one mask.For example, when the size of the mask is formed by a 5×5 pixel array,there will be 24 pixels other than the central pixel. Equation 5represents each of 24 pixels to which an integer serial number isattached. The four types of weight means can calculate Y_(wm) _(—) _(y),Y_(wm) _(—) _(ycbcr), Cb_(wm) _(—) _(ycbcr), and Cr_(wm) _(—) _(ycbcr)from Equation (5).

In correcting of the luminance and the chrominance of each pixel in stepS24, the color noise of the image is reduced by correcting the YCbCr ofeach pixel using the weight mean calculated at in step S23.

Generally, when the image signal is formed in an (Red Green Blue) (RGB)format, if the RGB components having any three pixels within the similarregion are (R1, G1, B1), (R2, G2, B2), and (R3, G3, B3), respectively,the following Equation (6) of the pixels is established between the RGBcomponents.

$\begin{matrix}{{\frac{R_{1}}{G_{1}} \simeq \frac{R_{2}}{G_{2}} \simeq \frac{R_{3}}{G_{3}} \simeq K^{\prime}}{\frac{B_{1}}{G_{1}} \simeq \frac{B_{2}}{G_{2}} \simeq \frac{B_{3}}{G_{3}} \simeq K^{''}}{\frac{B_{1}}{R_{1}} \simeq \frac{B_{2}}{R_{2}} \simeq \frac{B_{3}}{R_{3}} \simeq {{K^{\prime}}^{\prime}}^{\prime}}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

In Equation (6), K′, K″, and K′″ represent any constant. Additionally,relationships in Equation (7) may be derived by modifying Equation (6).

R₁−G₁≅R₂−G₂≅R₃−G₃≅K″″

B₁−G₁≅B₂−G₂≅B₃−G₃≅K′″″

B₁−R₁≅B₂−R₂≅B₃−R₃≅K″″″  Equation (7

In Equation (7), K″″, K′″″, and K″″″ represent any constant.Additionally, the YCbCr format is transformed by using an RGB format andthe relationship of Equation (7) may be applied to each component in theYCbCr format. In the YCbCr format, the relationship of each component isrepresented by Equation (8).

Y₁−Cb₁≅Y₂−Cb₂≅Y₃−Cb₃≅K₁

Y₁−Cr₁≅Y₂−Cr₂≅Y₃−Cr₃≅K₂  Equation (8)

In Equation (8), K1 and K2 represent any constant. The correlationbetween the YCbCr components of three pixels in Equation (8) is derivedunder the assumption that the three pixels are in similar regions.However, since the edge is actually present in the image and the pixelof the region having different characteristics is actually present inthe image, the relationship of the following Equation (9) may be derivedconsidering edge characteristics.

Y _(out) −Cb _(out) =Y _(wm) _(—) _(ycbcr) −Cb _(wm) _(—) _(ycbcr)

Y _(out) −Cr _(out) =Y _(wm) _(—) _(ycbcr) −Cr _(wm) _(—)_(ycbcr)  Equation (9)

If Equation (9) is arranged depending on Cb_(out) and Cr_(out), itbecomes Equation (10).

Cb _(out) =Cb _(wm) _(—) _(ycbcr)−(Y _(wm) _(—) _(ycbcr) −Y _(out))

Cr _(out) =Cr _(wm) _(—) _(ycbcr)−(Y _(wm) _(—) _(ycbcr) −Y_(out))  Equation (10)

By replacing Y_(out) with Y_(w) _(—) _(y) and imparting the weight to(Y_(wm) _(—) _(ycbcr)), Equation (10) can be converted to Equation (11).

Cb _(out) =Cb _(wm) _(—) _(ycbcr) −W _(cb)×(Y _(wm) _(—) _(ycbcr) −Y_(wm) _(—) _(y))

Cr _(out) =Cr _(wm) _(—) _(ycbcr) −W _(cr)×(Y _(wm) _(—) _(ycbcr) −Y_(wm) _(—) _(y))  Equation (11)

In Equation (11), W_(Cb), and W_(Cr) are a preset weight. Cb componentand Cr component from which noise is finally reduced by Equation (11) isobtained. As described above, the method for reducing color noise of animage may be implemented by reflecting the distance weight and the edgeweight through steps S21 to S24. The present invention provides a methodfor more effectively reducing color noise of an image than the existingmethods using only color information Cb and Cr, thereby making itpossible to more efficiently reduce color noise at the time ofphotographing images under a low luminance environment.

FIG. 4 is a diagram showing a device for reducing color noise accordingto an embodiment of the present invention.

As shown in FIG. 4, a device for reducing color noise of an imageaccording to an embodiment of the present invention includes a distanceweight calculator 41, an edge weight calculator 42, a weight meancalculator 43, and a color noise reducing unit 44.

The distance weight calculator 41 calculates a distance weight dependingon a distance from the central pixel in the mask having a predeterminedsize to each pixel in the mask in the input image. The predeterminedsize implies a pixel array of 5×5, 7×7, or the like. Provided that thecoordinates of the central pixel within the mask having thepredetermined size are (r_(c),c_(c)) and the coordinates of the pixel tobe calculated are (r,c), the distance weight is determined to be ininverse proportion to the distance between the coordinates of each pixelto be calculated from the coordinates of the central pixel.

Therefore, as the distance between the coordinates of the central pixeland the coordinates of each pixel to be calculated is increased, thedistance weight becomes smaller.

An example of the distance weight W_(d)(r,c) depending on the distancebetween the coordinates of the central pixel and the coordinates of eachpixel was described in Equation (2) above.

As represented by Equation (2), the distance weight W_(d)(r,c) may bedetermined to be in inverse proportion to the distance between thecoordinates of the central pixel and the coordinates of the pixel to becalculated, that is, between the coordinates of each pixel.Additionally, the relationship between them may be represented byvarious forms.

The reason the distance weight is introduced into the method forreducing color noise is to apply the color noise reducing ratiodifferentially according to the distance from the central pixel, withrespect to any two pixels where the luminance Y and the chrominance Cband Cr are changed at the same ratio based on the central pixel.

The edge weight calculator 42 calculates the edge weight depending onthe difference in size between the luminance and the chrominance of thecentral pixel and the luminance and the chrominance of every otherpixel.

In order to calculate the edge weight, an absolute value of thedifference in size between the YCbCr components forming the centralpixel and each pixel is first calculated. It is assumed that theluminance and the chrominance of the central pixel areY_(in)(r_(c),c_(c)) and Cb_(in)(r_(c),c_(c)) and Cr_(in)(r_(c),c_(c)),respectively, and the luminance and the chrominance of each pixel areY_(in)(r,c) and Cb_(in)(r,c) and Cr_(in)(r,c), respectively. In thiscase, absolute values D_(Y), D_(Cb), and D_(Cr) of the difference in theluminance and the chrominance between the central pixel and each pixelwere described in Equation (3).

Next, the edge weight W_(e) _(—) _(Y)(r,c) of luminance Y, the edgeweight We_Cb(r,c) of the chrominance Cb, and the edge weight W_(e) _(—)_(Cr)(r,c) of the chrominance Cr are calculated by using the absolutevalues D_(Y), D_(Cb), and D_(Cr) according to the difference in size ofthe calculated YCbCr signal.

Equation (4) describes the edge weights W_(e) _(—) _(Y)(r,c), W_(e) _(—)_(Cb)(r,c), and W_(e) _(—) _(Cr)(r,c) calculated depending on the presetthreshold values th_(e) _(—) _(Y), th_(e) _(—) _(Cb), and th_(e) _(—)_(Cr).

In Equation (4), if absolute value D_(Y) is threshold value th_(e) _(—)_(Y) or less, 0≦D_(Y)/th_(e) _(—) _(Y)≦1. The range of edge weight W_(e)_(—) _(Y)(r,c) value becomes 0≦W_(e) _(—) _(Y)(r,c)≦1. That is, whenD_(Y)=th_(e) _(—) _(Y), W_(e) _(—) _(Y)(r,c)=0 and when D_(Y)=0, W_(e)_(—) _(Y)(r,c)=1.

Similarly, if the absolute value D_(Cb) is the threshold value th_(e)_(—) _(Cb) or less, 0≦D_(Cb)/th_(e) _(—) _(Cb)≦1 and the range of edgeweight W_(e) _(—) _(Cb)(r,c) value becomes 0≦W_(e) _(—) _(Cb)(r,c)≦1.That is, when D_(Cb)=th_(e) _(—) _(Cb), W_(e) _(—) _(Cb)(r,c)=0 and whenD_(Cb)=0, W_(e) _(—) _(Cb)(r,c)=1.

Additionally, if the absolute value D_(Cr) is the threshold value th_(e)_(—) _(Cr) or less, 0≦D_(Cr)/th_(e) _(—) _(Cr)≦1 and the range of edgeweight W_(e) _(—) _(Cr)(r,c) value becomes 0≦W_(e) _(—) _(Cr)(r,c)≦1.That is, when D_(Cr)=th_(e) _(—) _(Cr), W_(e) _(—) _(Cr)(r,c)=0 and whenD_(Cr)=0, W_(e) _(—) _(Cr)(r,c)=1.

The weight mean calculator 43 calculates the mean weight of each pixelby using the calculated distance weight and the edge weight.

As described above, the weight mean according to the embodiment of thepresent invention is calculated according to Equation (5). As noted inEquation (5), the mean weight is calculated by depending on the abovefour types and the weights of each edge are changed according to whetherany one of the four types is used. In Equation (5), N in R_(N) and theC_(N) represents a serial number of each pixel in addition to thecentral pixel within one mask. For example, when the size of the mask isformed by a 5×5 pixel array, there are 24 pixels other than the centralpixel. Equation (5) represents each of 24 pixels to which an integerserial number is attached. The four types of mean weights can calculateY_(wm) _(—) _(y), Y_(wm) _(—) _(ycbcr), Cb_(wm) _(—) _(y), Cb_(wm) _(—)_(ycbcr), and Cr_(wm) _(—) _(ycbcr) from Equation (5).

The color noise reducing unit 44 reduces the color noise of the image bycorrecting the luminance (Y) and the chrominance (Cb, Cr) of each pixelby using the calculated weight mean.

The relationship between each component of YCbCr format is derived fromthe relationship between each component of the RGB format in a similarregion depending on Equations (6) to (8). However, since the edge may besubstantially present in the image and the pixel of the region havingdifferent characteristics may be substantially present therein, therelationship of each component of YCbCr is derived from Equations (9)and (10) considering the edge characteristics and Equation (11) isfinally derived by replacing Y_(out) with Y_(wm) _(—) _(y) and impartinga weight to (Y_(wm) _(—) _(ycbcr)−Y_(out)) in Equation (10).

That is, the Cb component and Cr component, from which color noise isreduced, is obtained by using the predetermined weights W_(Cb) andW_(Cr).

As described above, the apparatus for reducing color noise of an imageaccording to the embodiment of the present invention can moreeffectively reduce the color noise of the image than the method usingonly the color information Cb and Cr according to the related art,thereby making it possible to more efficiently reduce the color noise atthe time of photographing images under a low luminance environment.

FIG. 5 is a diagram showing an image photographing apparatus accordingto an embodiment of the present invention.

Referring to FIG. 5, the image photographing apparatus according to anembodiment of the present invention includes an image photographing unit51, an Image Signal Processor (ISP) 52, a device for reducing colornoise 40, a display unit 53, and a storage unit 54.

The image photographing unit 51 photographs an image for a subject andconverts it into an image signal. The image photographing unit includesa lens unit (not shown), an infrared blocking filter unit (not shown),and an image sensor (not shown).

The lens unit includes a plurality of lenses. Additionally, each of theplurality of lenses has a rotational symmetry with respect to an opticalaxis and the optical axes of the plurality of lenses are arranged on asingle axis. The plurality of lenses may be formed in a spherical or anaspherical shape. The lens unit may be formed of three lenses made of aplastic material.

The infrared blocking filter unit serves to block incident light in aninfrared band, which the human eye cannot see. The image sensorsensitively responds to the incident light of the infrared band (rayhaving about 750 nm of wavelength) that is not recognized by the humaneye. Therefore, since the image photographed by the image sensor wouldbe changed due to the incident light of the infrared band, the infraredblocking filter is used in order to block the incident light of theinfrared band.

The image sensor has a structure where the pixels having the sameconfiguration are disposed in an N×M matrix structure. An example of theimage sensor may include a CCD, a CMOS, or the like. The image sensor isoperated under a principle of producing color images by disposing athree primary filter in front of the image sensor and storinginformation passing through it and then summing it with the storedshading information in the image sensor.

The ISP 52 is an apparatus for converting the electrical signalconverted in the image sensor into the image signal. The image signalprocessor may be implemented in one chip when the image sensor is a CMOSand may be disposed in a chip in a DSP and SoC type. Special functions,such as a handshake preventing function, a low luminance compensatingfunction, or the like may be implemented.

A device for reducing color noise 40 includes a distance weightcalculator, an edge weight calculator, a weight mean calculator, and acolor noise reducing unit and includes the same components as the devicefor reducing color noise in FIG. 4.

The display unit 53 displays the image of the subject to be photographedor displays stored images. The display unit 53 may be implemented usinga Liquid Crystal Display (LCD), a Light Emitting Diode (LED), an OrganicLight Emitting Diode (OLED), or the like.

The storage unit 54 stores the image signal data photographed by theimage photographing unit 51. Additionally, the storage unit 54 may storea general program and applications for driving the image photographingapparatus. In particular, the storage unit 54 can store the aboveequations required to drive the device for reducing color noise includedin the image photographing apparatus and store an input/output look-uptable corresponding to the equation, such that the output valuescorresponding to the input values can also refer to the look-up table.

As described above, the image photographing apparatus according to theembodiment of the present invention can more effectively reduce colornoise of the image than a method using only the color information Cb andCr according to the related art, thus making it possible to moreefficiently reduce the color noise at the time of photographing imagesunder a low luminance environment.

Embodiments of the present invention may be implemented in the form ofhardware, software, and a combination thereof. Any such software may bestored, for example, in a volatile or non-volatile storage device suchas a ROM, a memory such as a RAM, a memory chip, a memory device, or amemory IC, or a recordable optical or magnetic medium such as a CD, aDVD, a magnetic disk, or a magnetic tape, regardless of erasability orre-recordability. It can also be appreciated that the storage device andthe storage medium are embodiments of machine-readable devices suitablefor storing a program including instructions that are executed by aprocessor device to thereby implement embodiments of the presentinvention. Therefore, embodiments of the present invention provide aprogram including codes for implementing a system or method claimed inany claim of the accompanying claims and a machine-readable device forstoring such a program. Further, this program may be electronicallyconveyed through any medium such as a communication signal transferredvia a wired or wireless connection, and embodiments of the presentinvention appropriately include equivalents thereto.

While the invention has been shown and described with reference tocertain embodiments thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the invention as definedby the appended claims and their equivalents.

1. A method for reducing color noise, comprising: calculating a distanceweight according to a distance from a central pixel to each pixel of aninput image in a mask having a predetermined size; calculating an edgeweight depending on a luminance and a chrominance between the centralpixel and each pixel; calculating a weight mean of each pixel by usingthe calculated distance weight and the edge weight; and correcting theluminance and the chrominance of each pixel by using the calculatedweight mean.
 2. The method of claim 1, wherein if the coordinates of thecentral pixel are (r_(c),c_(c)), the coordinates of each pixel are(r,c), and the distance weight of each pixel is W_(d)(r,c), the distanceweight is calculated by${W_{d}\left( {r,c} \right)} = {\frac{1}{\sqrt{\left( {r - r_{c}} \right)^{2} + \left( {c - c_{c}} \right)^{2}} + 1}.}$3. The method of claim 1, wherein if the luminance of the central pixelis Y_(in)(r_(c),c_(c)), the chrominance is Cb_(in)(r_(c),c_(c)) andCr_(in)(r_(c),c_(c)), the luminance of each pixel is Y_(in)(r,c), thechrominance of each pixel is Cb_(in)(r,c) and Cr_(in)(r,c), the absolutevalue of the difference in the luminance and the chrominance between thecentral pixel and each pixelD _(Y) =|Y _(in)(r _(c) ,c _(c))−Y _(in)(r,c)|D _(Cb) =|Cb _(in)(r _(c) ,c _(c))−Cb _(in)(r,c)| isD_(Cr)=|Cr_(in)(r_(c),c_(c))−Cr_(in)(r,c)|, and the edge weight ofluminance and the edge weight of chrominance for the threshold valueth_(e) _(—) _(Y), th_(e) _(—) _(Cb), and th_(e) _(—) _(Cr) is W_(e) _(—)_(Cb)(r,c) and W_(e) _(—) _(Cr)(r,c), respectively, the edge weight iscalculated by $\begin{matrix}\begin{matrix}{{W_{e\_ Y}\left( {r,c} \right)} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Y}}{{th}_{e\_ Y}} + 1} - 1} & {{{if}\mspace{14mu} D_{Y}} \geq {th}_{e\_ Y}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cb}}\left( {r,c} \right)}} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Cb}}{{th}_{e\_ {Cb}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cb}} \geq {th}_{e\_ {Cb}}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cr}}\left( {r,c} \right)}} = \left\{ \begin{matrix}{\frac{2}{\frac{D_{Cr}}{{th}_{e\_ {Cr}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cr}} \geq {th}_{e\_ {Cr}}} \\0 & {{otherwise}.}\end{matrix} \right.} \right.} \right.} & \;\end{matrix} & \;\end{matrix}$
 4. The method of claim 1, wherein the weight mean Y_(wm)_(—) _(y), Y_(wm) _(—) _(ycbcr), Cb_(wm) _(—) _(ycbcr)) and Cr_(wm) _(—)_(ycbcr) is calculated by$Y_{{wm}\_ y} = \frac{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}{\sum\limits_{y = y_{N}}^{y_{\; N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$$Y_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cb}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cb}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cr}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cr}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$5. The method of claim 4, wherein the calculated Y_(wm) _(—) _(y) isoutput as the chrominance of each pixel and the chrominance Cb_(out),and Cr_(out), of each pixel is calculatedCb _(out) =Cb _(wm) _(—) _(ycbcr) −W _(cb)×(Y _(wm) _(—) _(ycbcr) −Y_(wm) _(—) _(y)) by Cr_(out)=Cr_(wm) _(—) _(ycbcr)−W_(cr)×(Y_(wm) _(—)_(ycbcr)−Y_(wm) _(—) _(y)) and is output if the preset weight is W_(Cb)and W_(Cr).
 6. A device for reducing color noise, comprising: a distanceweight calculator calculating a distance weight according to a distancefrom a central pixel to each pixel of an input image in a mask having apredetermined size; an edge weight calculator calculating an edge weightdepending on luminance and chrominance between the central pixel andeach pixel; a weight mean calculator calculating a weight mean of eachpixel by using the calculated distance weight and the edge weight; and acolor noise reducing unit correcting the luminance and the chrominanceof each pixel by using the calculated weight mean.
 7. The device ofclaim 6, wherein if the coordinates of the central pixel are(r_(c),c_(c)), the coordinates of each pixel are (r,c), and the distanceweight of each pixel is W_(d)(r,c), the distance weight is calculated by${W_{d}\left( {r,c} \right)} = {\frac{1}{\sqrt{\left( {r - r_{c}} \right)^{2} + \left( {c - c_{c}} \right)^{2}} + 1}.}$8. The device of claim 6, wherein if the luminance of the central pixelis Y_(in)(r_(c),c_(c)), the chrominance is Cb_(in)(r_(c),c_(c)) andCr_(in)(r_(c),c_(c)), the luminance of each pixel is Y_(in)(r,c), thechrominance of each pixel is Cb_(in)(r,c) and Cr_(in)(r,c), the absolutevalue of the difference in the luminance and the chrominance between thecentral pixel and each pixelD _(Y) =|Y _(in)(r _(c) ,c _(c))−Y _(in)(r,c)|D _(Cb) =|Cb _(in)(r _(c) ,c _(c))−Cb _(in)(r,c)| isD_(Cr)=|Cr_(in)(r_(c),c_(c))−Cr_(in)(r,c)|, and the edge weight ofluminance and the edge weight of chrominance for the threshold valueth_(e) _(—) _(Y), th_(e) _(—) _(Cb), and th_(e) _(—) _(Cr) is W_(e) _(—)_(Cb)(r,c) and W_(e) _(—) _(Cr)(r,c), respectively, the edge weight iscalculated by $\begin{matrix}\begin{matrix}{{W_{e\_ Y}\left( {r,c} \right)} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Y}}{{th}_{e\_ Y}} + 1} - 1} & {{{if}\mspace{14mu} D_{Y}} \geq {th}_{e\_ Y}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cb}}\left( {r,c} \right)}} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Cb}}{{th}_{e\_ {Cb}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cb}} \geq {th}_{e\_ {Cb}}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cr}}\left( {r,c} \right)}} = \left\{ \begin{matrix}{\frac{2}{\frac{D_{Cr}}{{th}_{e\_ {Cr}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cr}} \geq {th}_{e\_ {Cr}}} \\0 & {{otherwise}.}\end{matrix} \right.} \right.} \right.} & \;\end{matrix} & \;\end{matrix}$
 9. The device of claim 8, wherein the weight mean Y_(wm)_(—) _(y), Y_(wm) _(—) _(ycbcr), Cb_(wm) _(—) _(ycbcr), and Cr_(wm) _(—)_(ycbcr) is calculated by$Y_{{wm}\_ y} = \frac{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}{\sum\limits_{y = y_{N}}^{y_{\; N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$$Y_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cb}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cb}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cr}_{{wm}\_ {ycbcr}} = {\frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cr}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}.}$10. The device of claim 9, wherein the calculated Y_(wm) _(—) _(y) isoutput as the luminance of each pixel and the chrominance Cb_(out) andCr_(out) of each pixel are calculatedCb _(out) =Cb _(wm) _(—) _(ycbcr) −W _(cb)×(Y _(wm) _(—) _(ycbcr) −Y_(wm) _(—) _(y)) depending on Cr_(out)=Cr_(wm) _(—)_(ycbcr)−W_(cr)×(Y_(wm) _(—) _(ycbcr)−Y_(wm) _(—) _(y)) and is output ifthe preset weight is W_(Cb) and W_(Cr).
 11. An image photographingapparatus, comprising: an image photographing unit photographing imagesand converting them into image signals; an image signal processorprocessing the images into image signals including luminance andchrominance; a device for reducing color noise correcting luminance andchrominance of each pixel by using a distance weight depending on adistance from a central pixel to each pixel of an input image and anedge weight depending on a difference in luminance and chrominancebetween the central pixel and each pixel of the image; a display unitoutputting an image signal from which color noise is reduced; and astorage unit storing the image signal from which color noise is reduced.12. The apparatus of claim 11, wherein the device for reducing colornoise comprises: a distance weight calculator calculating a distanceweight according to a distance from a central pixel to each pixel of aninput image; an edge weight calculator calculating an edge weightdepending on luminance and chrominance between the central pixel andeach pixel of the image; a weight mean calculator calculating a weightmean of each pixel by using the calculated distance weight and the edgeweight; and a color noise reducing unit correcting the luminance and thechrominance of each pixel by using the calculated weight mean.
 13. Theapparatus of claim 11, wherein if the coordinates of the central pixelare (r_(c),c_(c)), the coordinates of each pixel are (r,c), and thedistance weight of each pixel is W_(d)(r,c), the distance weight iscalculated by${W_{d}\left( {r,c} \right)} = {\frac{1}{\sqrt{\left( {r - r_{c}} \right)^{2} + \left( {c - c_{c}} \right)^{2}} + 1}.}$14. The apparatus of claim 11, wherein if the luminance of the centralpixel is Y_(in)(r_(c),c_(c)), the chrominance is Cb_(in)(r_(c),c_(c))and Cr_(in)(r_(c),c_(c)), the luminance of each pixel is Y_(in)(r,c),the chrominance of each pixel is Cb_(in)(r,c) and Cr_(in)(r,c), theabsolute value of the difference in the luminance and the chrominancebetween the central pixel and each pixelD _(Y) =|Y _(in)(r _(c) ,c _(c))−Y _(in)(r,c)|D _(Cb) =|Cb _(in)(r _(c) ,c _(c))−Cb _(in)(r,c)| isD_(Cr)=|Cr_(in)(r_(c),c_(c))−Cr_(in)(r,c)|, and the edge weight ofluminance and the edge weight of chrominance for the threshold valueth_(e) _(—) _(Y), th_(e) _(—) _(Cb), and th_(e) _(—) _(Cr) is W_(e) _(—)_(Cb)(r,c) and W_(e) _(—) _(Cr)(r,c), respectively, the edge weight iscalculated by $\begin{matrix}\begin{matrix}{{W_{e\_ Y}\left( {r,c} \right)} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Y}}{{th}_{e\_ Y}} + 1} - 1} & {{{if}\mspace{14mu} D_{Y}} \geq {th}_{e\_ Y}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cb}}\left( {r,c} \right)}} = \left\{ {{\begin{matrix}{\frac{2}{\frac{D_{Cb}}{{th}_{e\_ {Cb}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cb}} \geq {th}_{e\_ {Cb}}} \\0 & {otherwise}\end{matrix}{W_{e\_ {Cr}}\left( {r,c} \right)}} = \left\{ \begin{matrix}{\frac{2}{\frac{D_{Cr}}{{th}_{e\_ {Cr}}} + 1} - 1} & {{{if}\mspace{14mu} D_{Cr}} \geq {th}_{e\_ {Cr}}} \\0 & {{otherwise}.}\end{matrix} \right.} \right.} \right.} & \;\end{matrix} & \;\end{matrix}$
 15. The apparatus of claim 14, wherein the weight meanY_(wm) _(—) _(y), Y_(wm) _(—) _(ycbcr), Cb_(wm) _(—) _(ycbcr), andCr_(wm) _(—) _(ycbcr) is calculated by$Y_{{wm}\_ y} = \frac{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}{\sum\limits_{y = y_{N}}^{y_{\; N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$$Y_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {Y\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cb}_{{wm}\_ {ycbcr}} = \frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cb}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}$${Cr}_{{wm}\_ {ycbcr}} = {\frac{\begin{matrix}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times}}} \\{{W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {{Cr}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}\end{matrix}}{\sum\limits_{y = {- y_{N}}}^{y_{N}}{\sum\limits_{c = {- c_{N}}}^{c_{N}}{{W_{e\_ Y}\left( {r,c} \right)} \times {W_{e\_ {Cb}}\left( {r,c} \right)} \times {W_{e\_ {Cr}}\left( {r,c} \right)} \times {W_{d}\left( {r,c} \right)}}}}.}$16. The apparatus of claim 15, wherein the calculated Y_(wm) _(—) _(y),is output as the luminance of each pixel and the chrominance Cb_(out)and Cr_(out) of each pixel is calculatedCb _(out) =Cb _(wm) _(—) _(ycbcr) −W _(cb)×(Y _(wm) _(—) _(ycbcr) −Y_(wm) _(—) _(y)) by Cr_(out)=Cr_(wm) _(—) _(ycbcr)−W_(Cr)×(Y_(wm) _(—)_(ycbcr)−Y_(wm) _(—) _(y)) and is output if the preset weight is W_(Cb)and W_(Cr).